When using graphics applications, users often desire to manipulate an image by compositing objects into the images or performing scene reconstruction or modeling. To be effective and provide realistic results, these processes depend on recovering the illumination that contributes to the original image. Traditional methods of recovering illumination have been limited to illumination directly visible from the actual image. Such methods do not always provide a realistic illumination recovery because images often represent only a portion of the environment in which the scene resides, and the illumination of a scene may be formed by light sources that are not directly visible within an image itself. For example, many standard images provide a limited view, such as approximately a 60-degree view to a 90-degree view, while the entire 360-degree environment may include light sources illuminating the scene but that are outside that limited view shown in the image.
There are existing methods for estimating illumination from an entire panoramic environment, but they lack the robustness to be generally applicable to many indoor scenes. For example, current methods for recovery of outdoor scenes infer sky illumination and do not work for images of scenes with other types of illumination, such as indoor scenes. Additionally, there are data-driven approaches for indoor scenes that compare the illumination of an input image to known environment maps and determine the closest estimation between the known environment maps and the image. Such an approach, however, assumes that there will be a close estimate to the actual environment of the input image that is stored in the database of known environments. Considering the wide variety of illumination that occurs in indoor scenes, this assumption may not always hold true. Further, other methods utilize deep learning methods to recover reflectance maps, but these methods are based on a small amount of data and many assumptions that limit applicability of these methods. For instance, current deep learning methods for estimating illumination can be used only for images having a predefined object of a certain class. The predefined object must be at or near the center of the captured scene and must be segmented from the background. Such strong assumptions limit the use of these methods to a very narrow class of images. Accordingly, current systems do not provide accurate or robust methods for estimating illumination of a panoramic environment for a single image of an indoor scene.